I'm trying to estimate macroeconomic VAR model in R
using package vars
. Since I need to omit simultaneously all coefficients with t-ratio in absolute value less than 1.65, I used package function restrict
with adequate restriction matrix.
After that, $R^2$ for unemployment equation jumped from 29,2% (in the unrestricted model) to more than 61% (in the restricted model). That surprises me.
Why does this happen? If I estimate same equation individualy with OLS, $R^2$ stays approximately same (around 29%).
I read somewhere that restriction should be made with FGLS instead of OLS because OLS estimation could give inefficient coefficients. In R
restrict is done using OLS.
Can I use impulse response function and variance decomposition for analyzing relationships between variables and get valid results even if coefficients are inefficient?
Best Answer
In
method = ser
case, it estimates the equations repeatedly until all t-values are absolutely less thanthresh
. In this case:You are (probably) estimating the equation just once. It is equal to choosing
method = "manual"
and settingresmat
, based on insignificant parameters.The answer depends on the type of application. In this case, the results are inefficient, but how much inefficiency matters in your application?! However, I think the
refvar
function ofMTS package
generates the FGLS estimation of a restricted VAR.(Please note that I couldn't find any documentation for
refvar
, but by studying the source, it seems that it is a somehow a strangeFGLS
, because is uses identity matrix as the covariance matrix, although it estimates an unrestricted VAR first.)